Name: ALD 🦇 ATX | GENAI & INTELLIGENT AGENTS IN THE ENTERPRISE
Start: 2027-02-17T08:30:00.000-06:00
End: 2027-02-18T20:30:00.000-06:00
Location: Oracle

Official Data Science Salon now AI Loves Data Events Account

AI Loves Data

Join us at AI Loves Data Austin for an exclusive experience focused on 

GENAI & INTELLIGENT AGENTS IN THE ENTERPRISE 

AI Loves Data is a flagship Austin, TX event. The intimate one-day conference curates data science sessions to bring industry leaders and specialists face-to-face to educate each other on innovative solutions in generative AI, machine learning, predictive analytics and acceptance around best practices. You will see a mix of use-cases, technical talks and workshops, and you will walk away with actionable insights from those working on the frontlines of machine learning in the enterprise. All sessions are recorded and will be available on demand after the event.

 Dushyanth Sekhar, Head of AI & Data Platforms - Enterprise Data Organization at 

Talk: Multi Model Approach to Large Scale Information Retrieval

As AI adoption grows, relying on a single LLM for data extraction can lead to inconsistency and bias. This session explores how using multiple LLMs in parallel—combined with an automated LLM-as-a-Judge framework—can boost accuracy, reduce bias, and improve trust in extraction pipelines. Learn how to build scalable, resilient systems for unstructured data extraction without heavy human oversight.

🔸 10:30 AM – 10:50 AM | Cal Al-Dhubaib, Responsible AI & ML Executive at Further	

Devdas Gupta, Senior Manager Software Development and Engineering Lead at 

Talk: Why Agentic AI Works in Demos and Breaks in Enterprise Production

Agentic AI systems look impressive in demos. Agents reason, plan, collaborate, and act with apparent autonomy. Yet when these systems enter enterprise production, most fail under real world constraints.

This session is intended for technology leaders, engineers, and architects involved in designing or delivering Agentic AI systems at enterprise scale. It examines why Agentic AI breaks in production, focusing on architectural boundaries, governance, controlled tool execution, failure isolation, and operational ownership.

Attendees will gain practical insight and proven design patterns to move Agentic AI systems from experimental demos to reliable, secure, and scalable enterprise platforms.

Samaresh Kumar Singh, Principal Engineer at 

Practical Insights on Building Production Edge AI Systems

As AI scales, cloud-only inference often falls short on latency, privacy, and availability. This session presents a production-ready architecture for running AI at the edge—where data is generated—while using the cloud for training and governance. We’ll explore a three-tier cloud–edge–device stack, orchestration patterns (centralized, federated, agent-based), and low-latency techniques like quantization, early-exit inference, and pipeline parallelism. Attendees will walk away with actionable strategies to deploy fast, resilient AI across industrial, healthcare, and smart city applications.

Hamed Alikhani, Senior AI Engineer & Data Scientist at 

From Prompts to Production: Building Reliable AI Agents for the Enterprise

Agentic AI is no longer just a proof of concept. This session dives into how enterprises can design and deploy intelligent agents that are reliable, scalable, and safe in real-world environments.

Drawing from real production rollouts, we’ll unpack key architecture patterns—from task decomposition and tool orchestration to memory and routing logic. You’ll learn how to move past brittle prompt chains, implement robust validation layers, and design agents that deliver measurable outcomes while aligning with enterprise governance and cost constraints.

Perfect for technical leaders and practitioners ready to operationalize AI agents at scale.

Building a Culture of Evidence: Data Science, Experimentation, and Business Impact

As data-driven decision-making becomes a core strategic advantage, organizations are embedding experimentation—especially A/B testing—across product and business lifecycles. Unlike observational data, experimentation reveals causality, helping teams validate ideas, reduce risk, and drive evidence-based alignment. In this talk, I’ll share how structured experimentation helped the Instagram Delivery Data Science team connect system performance improvements to user engagement and revenue impact. Learn how to build rigorous, scalable experimentation programs that turn data into confident, high-impact decisions.

🔸 2:00 PM – 2:45 PM | Panel:  From Prototype to Production: Building AI That Actually Works in the Enterprise

Moderated by: Cal Al-Dhubaib, Responsible AI & ML Executive at 

Dippu Kumar SinghLeader Of Emerging Data TechnologiesFujitsu North America Inc.

Talk: Stop Building Generalists: Architecting Task-Specific Agents for Real-World Enterprise ROI

Generic LLMs often fall short in complex enterprise workflows. This session introduces AI Integration—a framework for replacing one-size-fits-all bots with task-specific agents that plug directly into high-value business processes like audits, supply chain ops, and SaaS platforms.

We’ll explore how to use Prompt Chaining, Semantic Routing, and Evaluator-Optimizer loops to design agents that are accurate, composable, and embedded into real systems. You’ll leave with a clear taxonomy of agent types and a practical roadmap for architecting AI systems that drive measurable ROI.

Reema Gill, Data/AI Governance Specialist at 

Talk: Scaling Responsibly: Building Data & AI Governance Frameworks for Fintech Startups That Grow into Enterprises

Fintech startups move fast—but as data access, AI adoption, and regulatory exposure grow, governance can’t wait. This session explores how emerging fintechs can implement right-sized AI and data governance frameworks that evolve with scale—without slowing innovation.

Drawing from experience across banks and high-growth startups, we’ll discuss how to operationalize global standards (EU AI Act, NIST AI RMF, OSFI E-23) to build trust with regulators, investors, and customers. The goal: make governance a catalyst, not a constraint.

Joyjit Roy, Lead Principal Technical Program Manager

Talk: Transforming Business with Agentic AI

This session explores how Agentic AI architectures move beyond rule-based workflows to enable enterprise-scale decision automation through adaptive planning, contextual memory, and closed-loop evaluation. It outlines key architectural components—guardrails, task decomposition, execution control, and feedback-driven refinement—and shows how these systems maintain auditability while operating under uncertainty. Real-world examples from eCommerce, insurance, and healthcare highlight measurable improvements in personalization, underwriting, fraud detection, and clinical coordination, with a focus on governance, human oversight, and production-ready deployment.

Hari Kishan, Director of Cloud Engineering at 

Talk: Inside Manulife’s Agentic Architecture: Real-Time Voice Automation Powered by RAG and Sentiment AI

While many enterprises talk about conversational AI, few have deployed it at scale—let alone in a highly regulated financial setting. At Manulife, that’s exactly what we’ve done.

This session dives into how a legacy Avaya IVR system was reimagined into an agentic, self-optimizing platform powered by Amazon Connect and RAG pipelines. Learn how we built SSML models that adjust tone and phrasing in real time, reduced AHT, and introduced an orchestration layer that updates strategies in minutes—not weeks.

If you’re scaling GenAI, modernizing CX, or looking to deploy agentic architectures in enterprise environments, this is your blueprint.

Yukti Goyal, Advanced Software Engineer at 

Talk: AI for Protecting Patient Data (PHI Security)

As healthcare data grows in volume and value, protecting PHI and ePHI has never been more critical—or more complex. In this session, we’ll explore how AI is transforming cybersecurity in healthcare, with advanced monitoring, anomaly detection, and adaptive defenses tailored for compliance-heavy environments like HIPAA.

Learn how techniques like UEBA, unsupervised learning, and AI-driven threat detection are being used to detect insider threats, stop credential misuse, and build resilient, compliant frameworks for modern healthcare systems.

If you’re working at the intersection of healthcare, AI, and data protection—this session is for you.

| Preetham Kaukuntla, Staff Data Scientist at 

Talk: Using LLM to Improve Email Marketing

Traditional rule-based notification systems fall short at scale. In this session, Preetham Reddy Kaukuntla shares how Glassdoor rebuilt its email and push infrastructure using ML models for both content and timing optimization. By integrating transformer-based subject line generation, LSTM and Prophet models for send-time prediction, and uplift modeling, Glassdoor now delivers 32M+ daily messages with measurable engagement gains. The session covers architecture, feature engineering, and lessons learned on model evaluation and long-term user impact.

Expect to walk away with tactical insights from those leading AI transformation within their organizations.

 is head quartered in Miami and is the leading provider of unique content and a diverse, vendor-neutral community for data scientists, machine learning engineers, and other subject matter experts. Since 2016, we have been driving knowledge sharing, best practices, and innovation in data science, machine learning and AI. With our extensive digital platform of content, webinars, training, and podcast, we cater to the evolving needs of our fast-growing and diverse community. Join us to access valuable resources, engage in insightful discussions, and be part of shaping the future of data science, machine learning and AI. 

Refunds are available up to 30 days prior to the start of the event.

Tickets may be transferred to another attendee up to 48 hours before the event. No exceptions.